Remote sensing –Beyond images Mexico 14-15 December 2013 The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)

1. Remote sensing based water management
from the watershed to the field level
Stephan J. Maas
Nithya Rajan
Texas Tech University
Texas A&M AgriLife Research
Texas A&M AgriLife Research
Vernon, TX
Lubbock, TX

2. Water Use
A key factor in water resource management is information on
how much water vegetation (crops, pastures, rangelands and
natural ecosystems) is using.
For example, for irrigation scheduling you need to know how
much water the crop is using so you can schedule irrigation to
replace it.
There are several ways to measure
or estimate water use-•
Lysimeters
•
Eddy covariance systems
•
Crop coefficient methods

3. Standard Crop Coefficient Method
Standard crop coefficients are designed to estimate crop ET under
“standard conditions” which represent “the upper envelope of crop ET
where no limitations are placed on crop growth or ET due to water
shortage, crop density, or disease, weed, insect, or salinity pressures”
(Allen et al., FAO-56,1998).
So, the standard crop coefficient approach can tell you how much water a
crop would be using if it were growing under “standard” (non-limited)
conditions, but it can’t tell you how much water the crop in a particular
field is actually using.

4. Spectral Crop Coefficient Method
Daily water use (WU) of vegetation can be estimated
using the “spectral crop coefficient” method:
WU = Ksp * PETfc * Fstress
Ksp is the spectral crop coefficient (value 0-1)
PETfc is the potential ET (well-watered full canopy)
Fstress is a stress factor (value 0-1)
Ksp is equivalent to the vegetation ground cover (GC).
Details of the procedure can be found in:
Rajan, N., S. J. Maas, and J. Kathilankal. 2010. Estimating crop water use of
cotton in the Texas High Plains. Agronomy Journal, Vol. 102, No. 6, p. 16411651.

5. Calculation of PETfc
Weather data for calculating PETfc can be obtained from
standard observing stations.
Air temperature
Humidity
Solar irradiance
Wind speed

6. Spectral Crop Coefficient
Vegetation GC (Ksp) can be estimated from readily
available multispectral remote sensing imagery.
Details of the procedure can be found in:
Maas, S. J., and N. Rajan. 2008. Estimating ground cover of field crops using mediumresolution multispectral satellite imagery. Agronomy Journal. 100(2): 320-327.

7. Spectral Crop Coefficient
Details of the procedure can be found in:
Rajan, N., and S. Maas. 2009. Mapping crop ground cover using airborne multispectral
digital imagery. Precision Agriculture. 10:304-318.

8. Validation of Ksp Method
Cotton
2008
2010
Rajan, N., and S. Maas. Spectral crop coefficient approach for estimating daily crop
water use. Agricultural Water Management. (in review).

9. Validation of Ksp Method
For irrigated crops or non-irrigated vegetation (such as dryland crops or
pastures) that are acclimated to their environment, Fstress ≈ 1.

10. Seasonal Water Use
For seasonal WU, you can sum daily WU estimates.
To get GC values between dates with RS observations—
• Interpolate (if you have relatively frequent RS observations)
• Use infrequent RS observations to scale basal spectral crop
coefficient curves
To develop basal Ksp curves, all you need is historic RS data.

11. Application
By choosing the appropriate source of remote sensing
imagery, you can estimate the WU of crops or other
vegetation at a variety of spatial scales.
Sub-field scale
Field scale
Regional or watershed scale
Remember-- to evaluate vegetation GC (Ksp), you only
need image DC data in the red and NIR spectral bands,
which is available from a wide variety of sensor
platforms.

12. Application- Sub-field Scale
Objective: Seasonal CWU for a 120-acre center-pivot
irrigated cotton field in the Texas High Plains
Source of RS data: Landsat TM

13. Application- Field Scale
Objective: Seasonal CWU for fields comprising the Texas Alliance
for Water Conservation (TAWC) Demonstration Project in the
Texas High Plains
Source of RS data:
Landsat TM

14. Application- Field Scale
Comparison of the
water used by
different crops.
Comparison of the
water used by a
crop under different
types of irrigation.

15. Application- Regional Scale
Objective: Seasonal WU for entire TAWC Project area
Source of RS data: Landsat TM
Vegetation accounted for a total of
18,370 acre-ft of water lost from the
project area during the May-Oct
growing season in 2005.

16. Application- Watershed Scale
Objective: Seasonal WU for Walnut Gulch Experimental
Watershed near Tombstone, Arizona
Source of RS data: AVHRR

17. SUMMARY and CONCLUSIONS
The spectral crop coefficient method is effective in
estimating the actual water use of crops and other
vegetation on a field-by-field basis.
This method uses the same weather data required by
the standard crop coefficient approach. The only
additional information required is an estimate of
vegetation ground cover.
Estimates of ground cover can be obtained from a
variety of remote sensing platforms.
It provides a way to evaluate the water use of vegetation
at a variety of spatial scales, from the field to the
watershed scale.

18. Acknowledgements
The authors wish to thank the Texas Alliance for Water Conservation
Demonstration Project, funded by the Texas Water Development Board,
for the resources necessary for conducting many of their projects.